We present here a system under development, the present goals of which are to assist (a) students in inductively learning a set of rules to generate sentences in French, and (b) psychologists in gathering data on natural language learning. Instead of claiming an all-encompassing model or theory, we prefer to elaborate a tool, which is general and flexible enough to permit the testing of various theories. By controlling parameters such as initial knowledge, the nature and order of the data, we can empirically determine how each parameter affects the efficiency of learning. Our ultimate goal is the modelling of human learning by machine. Learning is viewed as problem-solving, i.e. as the creation and reduction of a search-space. By integrating the student into the process, that is, by encouraging him to ask an expert (the system') certain kinds of questions, like: can one say x ? how does one say x ? why does one say x ?, we can enhance not only the efficiency of the learning, out also our understanding of the underlying processes. By having a trase of the whole dialogue (what questions have been asked at, what time), we should be able to infer the student's learning strategies.
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